2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA)最新文献_第2页

Multi instance learning via deep CNN for multi-class recognition of Alzheimer's disease 基于深度CNN的多实例学习用于阿尔茨海默病的多类别识别

2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA) Pub Date : 2019-11-01 DOI: 10.1109/IWCIA47330.2019.8955006

M. Kavitha, N. Yudistira, Takio Kurita

{"title":"Multi instance learning via deep CNN for multi-class recognition of Alzheimer's disease","authors":"M. Kavitha, N. Yudistira, Takio Kurita","doi":"10.1109/IWCIA47330.2019.8955006","DOIUrl":"https://doi.org/10.1109/IWCIA47330.2019.8955006","url":null,"abstract":"In recent years, number of classification techniques for Alzheimer's disease (AD) have been developed that produced methods based on the use of hand-crafted machine learning and obscure deep learning models. This study proposed a new classification framework based on the combination of Unet-like 2D convolutional neural networks (CNN) and multinomial logistic regression classifier, which learns the intra-slice for multi-class classification after the selection of the 3D positron emission tomography (PET) image into a sequence of 2D slices. The CNNs are performed to generate the attention features of the brain while the logistic regression incorporated to learn those specifically localized features of various classes for AD classification. At the end of the network, we used a average pooling layer before the softmax for four-class classification problem. It can efficiently generate a flexible class of transformations and that can be trained end-to-end by back propagation. The results indicated that the proposed multi-instance learning (MIL) learns region of interest (ROI) itself and thus that could help to efficiently identify the precise patterns for AD. The proposed combined Unet-like CNN with multinomial regression classifier approach achieved highest accuracy of 97.9% and 96.7% on the classification of AD and MCI, respectively. It is much higher than the performances of the conventional methods in the literature.","PeriodicalId":139434,"journal":{"name":"2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA)","volume":"326 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123826154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Diversifying experiences in multi agent reinforcement learning 多智能体强化学习的多样化经验

2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA) Pub Date : 2019-11-01 DOI: 10.1109/IWCIA47330.2019.8955073

N. A. V. Suryanarayanan, H. Iba

引用次数: 0

Using Label Information in a Genetic Programming Based Method for Acquiring Block Preserving Outerplanar Graph Patterns with Wildcards 基于标记信息的遗传规划方法获取带有通配符的保块外平面图模式

2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA) Pub Date : 2019-11-01 DOI: 10.1109/IWCIA47330.2019.8955031

Fumiya Tokuhara, Shiho Okinaga, T. Miyahara, Yusuke Suzuki, T. Kuboyama, Tomoyuki Uchida

引用次数: 1

Improved Binarization Using Morphology-driven Image Resizing and Decomposition 基于形态驱动的图像大小调整和分解的改进二值化

2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA) Pub Date : 2019-11-01 DOI: 10.1109/IWCIA47330.2019.8955018

Chang-Te Lin, Jung-Hua Wang, Chun-Shun Tseng, Shan-Chun Tsai, Chiao-Wei Lin, R. Huang

{"title":"Improved Binarization Using Morphology-driven Image Resizing and Decomposition","authors":"Chang-Te Lin, Jung-Hua Wang, Chun-Shun Tseng, Shan-Chun Tsai, Chiao-Wei Lin, R. Huang","doi":"10.1109/IWCIA47330.2019.8955018","DOIUrl":"https://doi.org/10.1109/IWCIA47330.2019.8955018","url":null,"abstract":"This paper presents a novel binarization algorithm for stained decipherable patterns. First, the input image is downsized, of which the reduction ratio is determined by iteratively applying binary morphological Closing operation. Such morphology-driven image downsizing not only saves the computation time of subsequent processes, but the key features necessary for the successful decoding is preserved. Then, high or low contrast areas are decomposed by applying the grayscale morphological Closing and Opening operators to the downsized image, and subtracting the two resulting output images from each other. If necessary, these areas are further subjected to decomposition to obtain finer separation of high and low regions. Having done the preprocessing, two approaches are proposed to do the binarization: (1) GMM is used to estimate a binarization threshold for each region (2) the binarization problem is treated as an image-translation task and hence a deep learning approach based on the conditional generative adversarial network (cGAN) is trained using the high or low contrast areas as conditional inputs. Our method solves the difficulty of choosing a proper preset sampling mask in conventional adaptive thresholding methods. Extensive experimental results show that the binarization algorithm can efficiently improve the decipher success rate over the other methods.","PeriodicalId":139434,"journal":{"name":"2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115630308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Code Completion for Programming Education based on Recurrent Neural Network 基于递归神经网络的编程教育的代码完成

2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA) Pub Date : 2019-11-01 DOI: 10.1109/IWCIA47330.2019.8955090

Kenta Terada, Y. Watanobe

引用次数: 13

Statistical Power Analysis for IoT Device Oriented Encryption with Glitch Canceller 基于故障消除器的物联网设备加密统计功率分析

2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA) Pub Date : 2019-11-01 DOI: 10.1109/IWCIA47330.2019.8955017

S. Takemoto, Y. Nozaki, M. Yoshikawa

引用次数: 1

Acquiring Multiagent Cooperative Behavior in the RoboCup Soccer Simulation 机器人世界杯足球仿真中多智能体合作行为的获取

2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA) Pub Date : 2019-11-01 DOI: 10.1109/IWCIA47330.2019.8955047

Hidehisa Akiyama

{"title":"Acquiring Multiagent Cooperative Behavior in the RoboCup Soccer Simulation","authors":"Hidehisa Akiyama","doi":"10.1109/IWCIA47330.2019.8955047","DOIUrl":"https://doi.org/10.1109/IWCIA47330.2019.8955047","url":null,"abstract":"The RoboCup Soccer Simulation is a research platform for multiagent systems and artificial intelligence. It is based on the RoboCup Soccer 2D Simulator, which enables two teams of 11 autonomous player agents and an autonomous coach agent to play a game of soccer with highly realistic rules and game play. The soccer simulation has devoted more attention to teamwork techniques than to robot control techniques. Therefore, we can avoid the burdens of developing and maintaining mechanical devices and also developing complex robot control tasks such as bipedal walking. These characteristics enable us to concentrate on research efforts related to teamwork. In order to acquire an appropriate teamwork, we need cooperative behavior models for coordinating teammate players, the methods to analyze opponent team behavior, and adaptaion techniques to the opponent strategy. As successful approaches to the first problem, we have proposed two important methods, a positioning model using triangulation and a sequential action planning using a tree-search method. These methods have already been implemented in our released code. In this talk, I introduce the effectiveness of these methods and the overview of our software. The recent research/development topics in the soccer simulation, especially opponent modeling and adaptation technique, are also introduced.","PeriodicalId":139434,"journal":{"name":"2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114295521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neurosynaptic Computational Elements for Adaptive Transient Synchrony: Biophysical Accuracy versus Hardware Complexity 自适应瞬态同步的神经突触计算元素:生物物理精度与硬件复杂性

2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA) Pub Date : 2019-11-01 DOI: 10.1109/IWCIA47330.2019.8955105

A. Zjajo

引用次数: 0

IEEE IWCIA2019 Organizing Committee IEEE IWCIA2019组委会

2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA) Pub Date : 2019-11-01 DOI: 10.1109/iwcia47330.2019.8955055

Tomohiro Hayashida, Keiichi Tamura, Tomoko Tateyama, J. Kushida, D. Hirotani

引用次数: 0

Multi-Channel MHLF: LSTM-FCN using MACD-Histogram with Multi-Channel Input for Time Series Classification 多通道mmhf:基于macd直方图的LSTM-FCN多通道时间序列分类

2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA) Pub Date : 2019-11-01 DOI: 10.1109/IWCIA47330.2019.8955030

Shuichi Hashida, Keiichi Tamura

引用次数: 7